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Enhancing Healthcare Process Model Discovery Through Duplicate Task Identification

2025-07-07Documento de conferência Acesso embargado
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dc.contributor.authorSu, Xuan
dc.contributor.authorLiu, Cong
dc.contributor.authorLu, Faming
dc.contributor.authorCheng, Long
dc.contributor.authorZeng, Qingtian
dc.contributor.authorZhou, Jiehan
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.coverage.spatialHelsinki, Finland
dc.date.accessioned2025-12-04T21:06:20Z
dc.date.embargoedUntil2027-09-30
dc.date.issued2025-07-07
dc.descriptionSu, X., Liu, C., Lu, F., Cheng, L., Zeng, Q., & Zhou, J. (2025). Enhancing Healthcare Process Model Discovery Through Duplicate Task Identification. In R. N. Chang, C. K. Chang, J. Yang, N. Atukorala, D. Chen, S. Helal, S. Tarkoma, Q. He, T. Kosar, C. A. Ardagna, A. Beheshti, B. Cheng, & W. Gaaloul (Eds.), 2025 IEEE International Conference on Web Services: IEEE ICWS 2025 (pp. 477-483). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICWS67624.2025.00067
dc.description.abstractHealthcare plays an increasingly vital role in our daily lives. Modern Hospital Information Systems (HISs) record and store detailed medical treatment processes for all patients in the form of event logs. Leveraging these logs, process mining techniques have been widely applied to extract valuable insights, optimize medical processes, and enhance healthcare service delivery. However, existing process discovery techniques struggle to effectively handle healthcare event logs, as these processes often involve a high frequency of duplicate tasks. To address these challenges, we propose a novel duplicate task-aware process discovery technique for healthcare. More specifically, it first analyzes contextual relations among tasks to identify the sequence of duplicate tasks. Then, duplicate tasks are relabeled using a transition system derived from the input event log. Finally, a directly-follows graph is generated based on transition adjacency relations and transformed into a Petri net using the Inductive Miner. The proposed technique is fully implemented in the opensource process mining platform ProM and evaluated using a public healthcare process case with six event logs. Comparative analysis with state-of-the-art discovery techniques demonstrates that our approach accurately identifies duplicate tasks and produces high-quality process models, achieving superior replay precision and reduced complexity.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent7
dc.format.extent1182380
dc.identifier.doi10.1109/ICWS67624.2025.00067
dc.identifier.isbn979-8-3315-5563-4
dc.identifier.otherPURE: 133048146
dc.identifier.otherPURE UUID: 9f9c4382-5b9b-45f9-b72d-d62bed618204
dc.identifier.otherScopus: 105018795238
dc.identifier.otherWOS: 001699536200057
dc.identifier.urihttp://hdl.handle.net/10362/191476
dc.identifier.urlhttps://www.scopus.com/pages/publications/105018795238
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001699536200057
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectDuplicate
dc.subjectHealthcare process
dc.subjectHospital information systems
dc.subjectModel discovery
dc.subjectProcess mining
dc.subjecttasks
dc.subjectInformation Systems
dc.subjectComputer Science Applications
dc.subjectComputer Networks and Communications
dc.subjectInformation Systems and Management
dc.subjectArtificial Intelligence
dc.titleEnhancing Healthcare Process Model Discovery Through Duplicate Task Identificationen
dc.typeconference object
degois.publication.firstPage477
degois.publication.lastPage483
degois.publication.title2025 IEEE International Conference on Web Services
degois.publication.titleIEEE International Conference on Web Services (ICWS) 2025
dspace.entity.typePublication
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